Local LLM with Ollama
Deploy Ollama locally, interact with multiple models, and build a Python client with streaming.
Lab Overview
In this lab, you'll deploy a local LLM environment using Ollama and build a Python client that communicates with it.
You'll learn to:
- Deploy and configure Ollama for local LLM inference
- Pull and manage multiple model versions
- Build a Python client that queries local models
- Implement streaming responses for better UX
- Compare model performance and select appropriate models
Prerequisites:
- Python programming basics
- Understanding of APIs and HTTP requests
- Completion of previous AI Foundations labs
Key Concepts:
- Local model inference vs cloud APIs
- Ollama REST API
- Streaming response patterns
- Model selection criteria
What You'll Learn
Deploy and configure Ollama for local LLM inference
Manage multiple models with Ollama CLI
Build a Python client that queries local LLMs
Implement streaming responses using async generators
Prerequisites
ai-python-environment
basic-python
Technologies Covered
Part of a Course
This lab is part of the AI Foundations for Infrastructure Engineers course
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